IT-Forum

Topic: 
Understanding synergistic interactions and complex information sharing
Friday, November 13, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202
Speaker: 
Fernando Rosas (KU Leuven)
Abstract / Description: 

The interactions between three or more variables are frequently nontrivial, poorly understood, and yet, are paramount for future advances in fields such as multiuser information theory, neuroscience and complexity science. In this talk, we will study the interactions that can exist between a group of random variables by introducing a axiomatic framework that characterizes the ways in which information can be shared. The framework is based on the novel notion of information synergy, which measures statistical structures that are present in the whole set of variables but not in the any of them individually. Finally, the framework will be applied to several communication scenarios, providing a more intuitive understanding of their fundamental limits.


 

The Information Theory Forum (IT-Forum) at Stanford ISL is an interdisciplinary academic forum which focuses on mathematical aspects of information processing. With a primary emphasis on information theory, we also welcome researchers from signal processing, learning and statistical inference, control and optimization to deliver talks at our forum. We also warmly welcome industrial affiliates in the above fields. The forum is typically held in Packard 202 every Friday at 1:00 pm during the academic year.

Fernando Rosas studied Music Composition (BMus 2002), Philosophy (Minor 2002), Mathematics (BSc Hons. 2006) and Electric Engineering (MSc and PhD 2008-2012) in the Pontificia Universidad Catolica de Chile. Now he working as a Postdoctoral Researcher in the group of microelectronics -MICAS- at KU Leuven University, Belgium. His researches ranges from the design of energy-efficient wireless acoustic sensor networks to the development of new theoretical notions of shared information. He is also interested in using Information Theory and Thermodynamics to enable a deeper understanding of self-organized systems.